[{"data":1,"prerenderedAt":824},["ShallowReactive",2],{"/en-us/blog/unreview-a-year-later-how-gitlab-is-being-transformed-by-ml-powered-code-review":3,"navigation-en-us":46,"banner-en-us":456,"footer-en-us":466,"blog-post-authors-en-us-Taylor McCaslin":705,"blog-related-posts-en-us-unreview-a-year-later-how-gitlab-is-being-transformed-by-ml-powered-code-review":720,"blog-promotions-en-us":762,"next-steps-en-us":814},{"id":4,"title":5,"authorSlugs":6,"authors":8,"body":10,"category":11,"categorySlug":11,"config":12,"content":16,"date":20,"description":17,"extension":27,"externalUrl":28,"featured":14,"heroImage":19,"isFeatured":14,"meta":29,"navigation":30,"path":31,"publishedDate":20,"rawbody":32,"seo":33,"slug":13,"stem":38,"tagSlugs":39,"tags":44,"template":15,"updatedDate":28,"__hash__":45},"blogPosts/en-us/blog/unreview-a-year-later-how-gitlab-is-being-transformed-by-ml-powered-code-review.yml","UnReview a year later: How GitLab is transforming DevOps code review with ML-powered functionality",[7],"taylor-mccaslin",[9],"Taylor McCaslin","\n\nA little over a year ago, [GitLab acquired UnReview](/press/releases/2021-06-02-gitlab-acquires-unreview-machine-learning-capabilities.html), a machine learning-based solution for automatically identifying [relevant code reviewers](/stages-devops-lifecycle/create/) and distributing review workloads and knowledge. Our goal is to integrate UnReview’s ML-powered code review features throughout GitLab, the One DevOps Platform. We checked in with Taylor McCaslin, principal product manager, ModelOps, at GitLab, to find out the impact UnReview has had so far and what comes next.\n\n**The idea of applying machine learning to code review was already underway at GitLab before the UnReview acquisition. What was it about ML/AI and automation that seemed a good fit for the code review process? How did the UnReview acquisition affect that strategy?**\n\nThe acquisition of UnReview gave GitLab a practical way to get started with a really focused value proposition that was obvious to the platform. ML/AI is a lot more than just having a useful algorithm. UnReview and its team gave GitLab talent with experience building MLOps pipelines and working with production DataOps workflows. As a source code management ([SCM](/solutions/source-code-management/)) and continuous integration ([CI](/topics/ci-cd/)) platform, MLOps and DataOps are key ambitions for our ModelOps stage. UnReview is the foundational anchor of our AI Assisted group, and we anticipate developing more ML-powered features with the base that we’ve built integrating UnReview into our One DevOps platform. If it’s something you manually set today within GitLab, we’ll consider suggestions and automations: suggested labels, assignees, issue relationships, etc. You can learn more about our plans on our [GitLab Duo documentation](https://docs.gitlab.com/user/gitlab_duo/).\n\n> You’re invited! Join us on June 23rd for the [GitLab 15 launch event](https://page.gitlab.com/fifteen) with DevOps guru Gene Kim and several GitLab leaders. They’ll show you what they see for the future of DevOps and The One DevOps Platform.\n\n**There were three specific objectives with the UnReview project when you first started:**\n- **Eliminate the time wasted manually searching for an appropriate code reviewer to review code changes.**\n- **Make optimum recommendations that consider the reviewers’ experience and optimize the review load across the team, which additionally facilitates knowledge sharing.**\n- **Provide analytics on the state of code review in the project, explaining why a particular code reviewer is recommended.**\n\n**Have you had to change or add to these in any way?**\n\nWe now have Suggested Reviewers running for external beta customers as well as dogfooding it internally. We’ve learned a lot about what makes a good code reviewer. Some of the obvious things like context with the changed files and history of committing to that area of code are obvious. But there are less obvious things like what type of code someone has experience with (front-end or back-end).\n\nWe’re finding the concept of recency interesting: the idea that people who more recently interacted with files and functions may be better suited to review the code. Also, people leave companies, and that’s usually not something that can be inferred by the source graph, so we’re working on merging additional GitLab activity data with the recommendation engine.\n\nIn addition, we’re thinking a lot about bias in our recommendations. For example, a senior engineer likely has the most commits across a project, but we don’t always want to recommend a senior engineer. The more we work with the algorithm and recommendations, the more nuanced we find it.\n\nNot every organization does code review the same way, so we’re considering building different models for those that have no process versus organizations that have very rigid and hierarchical reviewer requirements. We also have to consider how recommendations interact with other features of the platform like code owners, maintainer roles, and commit access.\n\nWe’ve never been more excited about the potential of machine learning within GitLab. Some of the feedback we’ve had from beta customers are “this feels like magic” and that honestly encapsulates what we’re going for. Sometimes the right code reviewer is just a feeling that you can’t quite put your finger on. Through data and a little bit of magic, we may see Suggested Reviewers help speed up workflows, and cut down on back and forth and wasted time trying to find someone to do a great review of your code.\n\n**Introducing ML-powered features can come with challenges, especially being GitLab’s first data science feature. Can you speak to some of those challenges and how the team overcame them?**\n\nIt has been about a year since we closed the transaction. During that time period we’ve introduced a lot of new concepts to GitLab. Access to real-time data within the feature with DataOps extraction and cleaning of platform activity data. We have an end-to-end MLOps pipeline running 100% within GitLab CI that extracts, builds, trains, and deploys the UnReview model, and new observability metrics to know if the whole system is working. These are all foundational concepts that we’ve had to build from the ground up.\n\nAlso, we’ve introduced Python to the GitLab tech stack and have to develop new engineering standards and hiring interview practices to find the right talent for this team. We’re now turning the corner of this foundational work and I anticipate that relatively soon we’ll release Suggested Reviewers fully integrated with the platform and UI.\n\nMilestones have been part of the way we’ve sliced up the integration work. We have a variety of internal milestones we’ve been tracking against, including porting the model into GitLab SCM and CI, building the Dataops and MLOps pipelines, and internal and external customer betas. It’s helpful to have these milestones to know what’s most important at any given time and not to get overwhelmed with all the moving pieces. We’re paving a new path with ML-powered features at GitLab, and once we’re done we’ll have a repeatable process and template to replicate over and over with new data science-powered features.\n\n**What has been the most surprising thing you’ve encountered or learned since UnReview first debuted?**\n\nCode Reviewers are foundational to the software development lifecycle. We thought this would be a really straightforward feature, but it turns out people REALLY care about recommendations. People hate bad suggestions so when the recommendations are wrong, the feedback is fast and furious. But when it’s right, it feels like magic. That really surprised me how positively people respond to a great suggestion.\n\nA lot of GitLab users have asked me what our success metric is for Suggested Reviewers. It should just feel like magic. Maybe you don’t know why someone was chosen, but you just feel they were the right person to review the change. And hopefully that leads to a more thoughtful code review, reduces the back and forth of trying to find someone to review your code, and ultimately creates a better experience end-to-end. A lot of engineers dread code reviews; we want to change that. I hope Suggested Reviewers can take the pain out of the experience and make it something engineers look forward to. That’s the feeling we’re trying to create with our recommendations. Obvious but magic.\n\n**What’s next for UnReview specifically and DevOps code review more generally? Where do you see the next big advances happening?**\n\nWe’re just scratching the surface. There are so many opportunities for recommendations and automations across the platform. We have a lot of data at GitLab, from the source graph, contribution history, CI builds, test logs, security scans, and deployment data. We believe all of this can be integrated together. I’m particularly excited about what we’re calling [Intelligent Code Security](https://about.gitlab.com/topics/agentic-ai/ai-code-analysis/). The idea is that we will be able to look at your source code as you’re writing it, analyze it for security vulnerabilities, and not only suggest fixes to common security flaws, but also apply that change, run your CI, confirm the build succeeds, confirm the vulnerability was resolved, and possibly even deploy that change, all automatically.\n\nImagine the future where your code gets more secure automatically while you sleep. That sounds wild, but we have the data to power a feature like this in the future. Suggested Reviewers is just the beginning. We haven’t seen many DevOps platforms fully embrace the data, code, and activity data that they have in a material way. I think we’ll see a lot more in this space moving forward as development platforms identify the massive opportunities to drive efficiencies and remove the frustrating parts of software development from the process.\n","devsecops",{"slug":13,"featured":14,"template":15},"unreview-a-year-later-how-gitlab-is-being-transformed-by-ml-powered-code-review",false,"BlogPost",{"title":5,"description":17,"authors":18,"heroImage":19,"date":20,"body":10,"category":11,"tags":21},"Learn how last year's acquisition has resulted in impactful features for the One DevOps Platform.",[9],"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749668002/Blog/Hero%20Images/pg-gear.jpg","2022-06-02",[22,23,24,25,26],"DevOps","code review","CI","integrations","AI/ML","yml",null,{},true,"/en-us/blog/unreview-a-year-later-how-gitlab-is-being-transformed-by-ml-powered-code-review","seo:\n  title: >-\n    GitLab transforms code review with machine learning tools\n  description: >-\n    Learn how last year's acquisition has resulted in impactful features for the\n    One DevOps Platform.\n  ogTitle: >-\n    GitLab transforms code review with machine learning tools\n  ogDescription: >-\n    Learn how last year's acquisition has resulted in impactful features for the\n    One DevOps Platform.\n  noIndex: false\n  ogImage: >-\n    https://res.cloudinary.com/about-gitlab-com/image/upload/v1749668002/Blog/Hero%20Images/pg-gear.jpg\n  ogUrl: >-\n    https://about.gitlab.com/blog/unreview-a-year-later-how-gitlab-is-being-transformed-by-ml-powered-code-review\n  ogSiteName: https://about.gitlab.com\n  ogType: article\n  canonicalUrls: >-\n    https://about.gitlab.com/blog/unreview-a-year-later-how-gitlab-is-being-transformed-by-ml-powered-code-review\ncontent:\n  title: >-\n    UnReview a year later: How GitLab is transforming DevOps code review with\n    ML-powered functionality\n  description: >-\n    Learn how last year's acquisition has resulted in impactful features for the\n    One DevOps Platform.\n  authors:\n    - Taylor McCaslin\n  heroImage: >-\n    https://res.cloudinary.com/about-gitlab-com/image/upload/v1749668002/Blog/Hero%20Images/pg-gear.jpg\n  date: '2022-06-02'\n  body: >\n\n\n    A little over a year ago, [GitLab acquired\n    UnReview](/press/releases/2021-06-02-gitlab-acquires-unreview-machine-learning-capabilities.html),\n    a machine learning-based solution for automatically identifying [relevant\n    code reviewers](/stages-devops-lifecycle/create/) and distributing review\n    workloads and knowledge. Our goal is to integrate UnReview’s ML-powered code\n    review features throughout GitLab, the One DevOps Platform. We checked in\n    with Taylor McCaslin, principal product manager, ModelOps, at GitLab, to\n    find out the impact UnReview has had so far and what comes next.\n\n\n    **The idea of applying machine learning to code review was already underway\n    at GitLab before the UnReview acquisition. What was it about ML/AI and\n    automation that seemed a good fit for the code review process? How did the\n    UnReview acquisition affect that strategy?**\n\n\n    The acquisition of UnReview gave GitLab a practical way to get started with\n    a really focused value proposition that was obvious to the platform. ML/AI\n    is a lot more than just having a useful algorithm. UnReview and its team\n    gave GitLab talent with experience building MLOps pipelines and working with\n    production DataOps workflows. As a source code management\n    ([SCM](/solutions/source-code-management/)) and continuous integration\n    ([CI](/topics/ci-cd/)) platform, MLOps and DataOps are key ambitions for our\n    ModelOps stage. UnReview is the foundational anchor of our AI Assisted\n    group, and we anticipate developing more ML-powered features with the base\n    that we’ve built integrating UnReview into our One DevOps platform. If it’s\n    something you manually set today within GitLab, we’ll consider suggestions\n    and automations: suggested labels, assignees, issue relationships, etc. You\n    can learn more about our plans on our [GitLab Duo documentation](https://docs.gitlab.com/user/gitlab_duo/).\n\n\n    > You’re invited! Join us on June 23rd for the [GitLab 15 launch\n    event](https://page.gitlab.com/fifteen) with DevOps guru Gene Kim and\n    several GitLab leaders. They’ll show you what they see for the future of\n    DevOps and The One DevOps Platform.\n\n\n    **There were three specific objectives with the UnReview project when you\n    first started:**\n\n    - **Eliminate the time wasted manually searching for an appropriate code\n    reviewer to review code changes.**\n\n    - **Make optimum recommendations that consider the reviewers’ experience and\n    optimize the review load across the team, which additionally facilitates\n    knowledge sharing.**\n\n    - **Provide analytics on the state of code review in the project, explaining\n    why a particular code reviewer is recommended.**\n\n\n    **Have you had to change or add to these in any way?**\n\n\n    We now have Suggested Reviewers running for external beta customers as well\n    as dogfooding it internally. We’ve learned a lot about what makes a good\n    code reviewer. Some of the obvious things like context with the changed\n    files and history of committing to that area of code are obvious. But there\n    are less obvious things like what type of code someone has experience with\n    (front-end or back-end).\n\n\n    We’re finding the concept of recency interesting: the idea that people who\n    more recently interacted with files and functions may be better suited to\n    review the code. Also, people leave companies, and that’s usually not\n    something that can be inferred by the source graph, so we’re working on\n    merging additional GitLab activity data with the recommendation engine.\n\n\n    In addition, we’re thinking a lot about bias in our recommendations. For\n    example, a senior engineer likely has the most commits across a project, but\n    we don’t always want to recommend a senior engineer. The more we work with\n    the algorithm and recommendations, the more nuanced we find it.\n\n\n    Not every organization does code review the same way, so we’re considering\n    building different models for those that have no process versus\n    organizations that have very rigid and hierarchical reviewer requirements.\n    We also have to consider how recommendations interact with other features of\n    the platform like code owners, maintainer roles, and commit access.\n\n\n    We’ve never been more excited about the potential of machine learning within\n    GitLab. Some of the feedback we’ve had from beta customers are “this feels\n    like magic” and that honestly encapsulates what we’re going for. Sometimes\n    the right code reviewer is just a feeling that you can’t quite put your\n    finger on. Through data and a little bit of magic, we may see Suggested\n    Reviewers help speed up workflows, and cut down on back and forth and wasted\n    time trying to find someone to do a great review of your code.\n\n\n    **Introducing ML-powered features can come with challenges, especially being\n    GitLab’s first data science feature. Can you speak to some of those\n    challenges and how the team overcame them?**\n\n\n    It has been about a year since we closed the transaction. During that time\n    period we’ve introduced a lot of new concepts to GitLab. Access to real-time\n    data within the feature with DataOps extraction and cleaning of platform\n    activity data. We have an end-to-end MLOps pipeline running 100% within\n    GitLab CI that extracts, builds, trains, and deploys the UnReview model, and\n    new observability metrics to know if the whole system is working. These are\n    all foundational concepts that we’ve had to build from the ground up.\n\n\n    Also, we’ve introduced Python to the GitLab tech stack and have to develop\n    new engineering standards and hiring interview practices to find the right\n    talent for this team. We’re now turning the corner of this foundational work\n    and I anticipate that relatively soon we’ll release Suggested Reviewers\n    fully integrated with the platform and UI.\n\n\n    Milestones have been part of the way we’ve sliced up the integration work.\n    We have a variety of internal milestones we’ve been tracking against,\n    including porting the model into GitLab SCM and CI, building the Dataops and\n    MLOps pipelines, and internal and external customer betas. It’s helpful to\n    have these milestones to know what’s most important at any given time and\n    not to get overwhelmed with all the moving pieces. We’re paving a new path\n    with ML-powered features at GitLab, and once we’re done we’ll have a\n    repeatable process and template to replicate over and over with new data\n    science-powered features.\n\n\n    **What has been the most surprising thing you’ve encountered or learned\n    since UnReview first debuted?**\n\n\n    Code Reviewers are foundational to the software development lifecycle. We\n    thought this would be a really straightforward feature, but it turns out\n    people REALLY care about recommendations. People hate bad suggestions so\n    when the recommendations are wrong, the feedback is fast and furious. But\n    when it’s right, it feels like magic. That really surprised me how\n    positively people respond to a great suggestion.\n\n\n    A lot of GitLab users have asked me what our success metric is for Suggested\n    Reviewers. It should just feel like magic. Maybe you don’t know why someone\n    was chosen, but you just feel they were the right person to review the\n    change. And hopefully that leads to a more thoughtful code review, reduces\n    the back and forth of trying to find someone to review your code, and\n    ultimately creates a better experience end-to-end. A lot of engineers dread\n    code reviews; we want to change that. I hope Suggested Reviewers can take\n    the pain out of the experience and make it something engineers look forward\n    to. That’s the feeling we’re trying to create with our recommendations.\n    Obvious but magic.\n\n\n    **What’s next for UnReview specifically and DevOps code review more\n    generally? Where do you see the next big advances happening?**\n\n\n    We’re just scratching the surface. There are so many opportunities for\n    recommendations and automations across the platform. We have a lot of data\n    at GitLab, from the source graph, contribution history, CI builds, test\n    logs, security scans, and deployment data. We believe all of this can be\n    integrated together. I’m particularly excited about what we’re calling\n    [Intelligent Code Security](https://about.gitlab.com/topics/agentic-ai/ai-code-analysis/). The idea is\n    that we will be able to look at your source code as you’re writing it,\n    analyze it for security vulnerabilities, and not only suggest fixes to\n    common security flaws, but also apply that change, run your CI, confirm the\n    build succeeds, confirm the vulnerability was resolved, and possibly even\n    deploy that change, all automatically.\n\n\n    Imagine the future where your code gets more secure automatically while you\n    sleep. That sounds wild, but we have the data to power a feature like this in the future. Suggested Reviewers is\n    just the beginning. We haven’t seen many DevOps platforms fully embrace the\n    data, code, and activity data that they have in a material way. I think\n    we’ll see a lot more in this space moving forward as development platforms\n    identify the massive opportunities to drive efficiencies and remove the\n    frustrating parts of software development from the process.\n  category: devsecops\n  tags:\n    - DevOps\n    - code review\n    - CI\n    - integrations\n    - AI/ML\nconfig:\n  slug: >-\n    unreview-a-year-later-how-gitlab-is-being-transformed-by-ml-powered-code-review\n  featured: false\n  template: BlogPost\n",{"title":34,"description":17,"ogTitle":34,"ogDescription":17,"noIndex":14,"ogImage":19,"ogUrl":35,"ogSiteName":36,"ogType":37,"canonicalUrls":35},"GitLab transforms code review with machine learning 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inclusion and belonging (DIB)",{"href":681,"dataGaName":682,"dataGaLocation":474},"/diversity-inclusion-belonging/","Diversity, inclusion and belonging",{"text":338,"config":684},{"href":340,"dataGaName":341,"dataGaLocation":474},{"text":348,"config":686},{"href":350,"dataGaName":351,"dataGaLocation":474},{"text":353,"config":688},{"href":355,"dataGaName":356,"dataGaLocation":474},{"text":690,"config":691},"Modern Slavery Transparency Statement",{"href":692,"dataGaName":693,"dataGaLocation":474},"https://handbook.gitlab.com/handbook/legal/modern-slavery-act-transparency-statement/","modern slavery transparency 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Mccaslin",{"template":710},"BlogAuthor",{"name":9,"config":712},{"headshot":713,"ctfId":714},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749667996/Blog/Author%20Headshots/tmccaslin-headshot.png","tmccaslin",{},"/en-us/blog/authors/taylor-mccaslin",{},"en-us/blog/authors/taylor-mccaslin","7SPWjJi2CicE7Or_JC0bA95HjfK2vx1NkcbRlLVcgCk",[721,734,748],{"content":722,"config":732},{"title":723,"description":724,"authors":725,"heroImage":727,"date":728,"body":729,"category":11,"tags":730},"Teaching software development the easy way using GitLab","Learn how University of Washington lecturer Stephen G. Dame uses GitLab for Education to manage student assignments, distribute course materials, and provide inline code feedback at scale.\n",[726],"Rod Burns","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749659537/Blog/Hero%20Images/display-article-image-0679-1800x945-fy26.png","2026-04-29","For instructors teaching software development, one of the biggest logistical challenges is assignment distribution and feedback at scale. How do you give large groups of students access to course materials, keep solution code private, and still deliver meaningful, contextual feedback without lots of administrative overhead?\n\nThe **[GitLab for Education program](https://about.gitlab.com/solutions/education/)** provides qualifying institutions with free access to **GitLab Ultimate**, enabling instructors to build professional-grade workflows that mirror real-world software development environments. In this article, you'll learn how Stephen G. Dame, a lecturer in the Computing and Software Systems department at the University of Washington, Bothell, uses simple workflows in GitLab to manage everything from course materials to student feedback across multiple classes.\n\n## From aerospace to academia: Bringing GitLab to the classroom\n\nDame came to academia with years of experience as a chief software engineer at Boeing Commercial Airplanes, where GitLab was used for aerospace projects. As an adjunct professor, he became an early advocate for GitLab within the university, joining the GitLab for Education program to access the full feature set needed to run structured, scalable course workflows.\n\n> **\"GitLab provides the greatest way to organize multiple classes, student assignments, lectures, and code samples through the use of Groups and Subgroups, which I found to be unique to GitLab compared to other repository platforms.\"**\n>\n> - Stephen G. Dame, University of Washington, Bothell\n\n## Set up groups: Build the right structure before writing a line of code\n\nThe foundation of an effective GitLab-based course is a well-planned group hierarchy. GitLab's **[Groups and Subgroups](https://docs.gitlab.com/tutorials/manage_user/#create-the-organization-parent-group-and-subgroups)** allow instructors to model the natural structure of a university department institution, course, and role with precise, inheritable permissions at every level.\n\nDame's structure places the university at the root (`UWTeaching`), with each course occupying its own subgroup (e.g. `css430`). Within each course sit repositories for `lecture-materials` and `code`, alongside dedicated Subgroups for `students` and `graders`. Instructor materials remain private, while student and grader subgroups are configured with controlled permissions so that assignment briefs and solutions are visible only to the right people.\n\n![Screenshot of GitLab group hierarchy — institution, course subgroup, and per-student subgroups](https://res.cloudinary.com/about-gitlab-com/image/upload/v1777463673/dpxfnitv76pdmvcqtgag.png)\n\nPermissions cascade downward through the hierarchy via **Manage > Members**, allowing Dame to add students to a course's `students` subgroup with `Reporter` access and an expiration date tied to the end of the academic quarter. Students can clone and pull from assignment repositories but cannot push — keeping solution code firmly under instructor control.\n\nStudents are guided to set up SSH keys across all their working environments (local machines, cloud shells, virtual machines) so they can clone repositories and receive weekly updates via `git pull`. They copy relevant code into their own private repositories to manage their own version history.\n\n**Tip for large classes:** For larger cohorts, adding students by hand is impractical. GitLab's REST API lets you automate subgroup creation and membership from a list of usernames. Below is a sample Python script that handles this:\n\n```python\n    import gitlab\n    from datetime import datetime\n\n    # Connect to your GitLab instance\n    gl = gitlab.Gitlab('https://gitlab.com', private_token='YOUR_PRIVATE_TOKEN')\n\n    # Target parent group ID (e.g., the ID for \"css430 > students\")\n    parent_group_id = 12345678\n\n    # Set expiration: typically the beginning of the next month after quarter end\n    expiry_date = '2025-01-01'\n\n    # List of collected student usernames\n    student_list = ['alice_css430', 'bob_css430', 'carol_css430', 'dave_css430', 'eve_css430']\n\n    for username in student_list:\n        try:\n            # 1. Create a personal subgroup for the student\n            subgroup = gl.groups.create({\n                'name': username,\n                'path': username,\n                'parent_id': parent_group_id,\n                'visibility': 'private'\n            })\n\n            # 2. Add student to the new subgroup with Expiration\n            user = gl.users.list(username=username)[0]\n            subgroup.members.create({\n                'user_id': user.id,\n                'access_level': gitlab.const.REPORTER_ACCESS,\n                'expires_at': expiry_date\n            })\n            print(f\"Success: Subgroup created and student added for {username}\")\n        except Exception as e:\n            print(f\"Error processing {username}: {e}\")\n```\nThere is also an [open source project that automates class management](https://gitlab.com/edu-docs/class-management-automation) published by GitLab that provides additional tooling for this workflow.\n## Give feedback where the work actually lives\n\nOnce the structure is in place, the feedback workflow is where GitLab's value becomes most apparent to students. Dame asks students to submit assignments by opening a **[merge request](https://docs.gitlab.com/user/project/merge_requests/)** in their repository. This gives instructors an immediate, clean diff of everything the student has written.\n![A GitLab merge request showing inline code comment function for an instructor](https://res.cloudinary.com/about-gitlab-com/image/upload/v1777467468/icclzyglbkwlvfysggbi.png)\nInstructors can click any line of code and leave an **inline comment** — not just flagging what is wrong, but explaining why, and pointing to what to look at next. Students receive this feedback in direct context with their code, which is far more actionable than a comment at the bottom of a submitted document.\n\n## Join GitLab for Education\n\nSetting up your first GitLab assignment takes some initial effort, but once the structure is in place it largely runs itself. The real payoff goes beyond organization: Students graduate having worked daily in an environment that mirrors professional software development, building habits around [version control](https://about.gitlab.com/topics/version-control/) and [code review](https://docs.gitlab.com/development/code_review/) rather than learning them as abstract concepts.\n\nIf you are just getting started, keep it simple. Begin with a single course group, one assignment template, and a basic pipeline. The structure will grow naturally alongside your confidence with the platform.\n\nMake sure to **[sign up for GitLab for Education](https://about.gitlab.com/solutions/education/join/)** so that you and your students can access all top-tier features, including unlimited reviewers on merge requests, additional compute minutes, and expanded storage.\n\n> [Apply to the GitLab for Education program today](https://about.gitlab.com/solutions/education/join/).",[627,731],"open source",{"featured":14,"template":15,"slug":733},"teaching-software-development-the-easy-way-using-gitlab",{"content":735,"config":746},{"description":736,"authors":737,"heroImage":739,"date":740,"title":741,"body":742,"category":11,"tags":743},"AI-generated code is 34% of development work. Discover how to balance productivity gains with quality, reliability, and security.",[738],"Manav Khurana","https://res.cloudinary.com/about-gitlab-com/image/upload/v1767982271/e9ogyosmuummq7j65zqg.png","2026-01-08","AI is reshaping DevSecOps: Attend GitLab Transcend to see what’s next","AI promises a step change in innovation velocity, but most software teams are hitting a wall. According to our latest [Global DevSecOps Report](https://about.gitlab.com/developer-survey/), AI-generated code now accounts for 34% of all development work. Yet 70% of DevSecOps professionals report that AI is making compliance management more difficult, and 76% say agentic AI will create unprecedented security challenges.\n\nThis is the AI paradox: AI accelerates coding, but software delivery slows down as teams struggle to test, secure, and deploy all that code.\n\n## Productivity gains meet workflow bottlenecks\nThe problem isn't AI itself. It's how software gets built today. The traditional DevSecOps lifecycle contains hundreds of small tasks that developers must navigate manually: updating tickets, running tests, requesting reviews, waiting for approvals, fixing merge conflicts, addressing security findings. These tasks drain an average of seven hours per week from every team member, according to our research.\n\nDevelopment teams are producing code faster than ever, but that code still crawls through fragmented toolchains, manual handoffs, and disconnected processes. In fact, 60% of DevSecOps teams use more than five tools for software development overall, and 49% use more than five AI tools. This fragmentation creates collaboration barriers, with 94% of DevSecOps professionals experiencing factors that limit collaboration in the software development lifecycle.\n\nThe answer isn't more tools. It's intelligent orchestration that brings software teams and their AI agents together across projects and release cycles, with enterprise-grade security, governance, and compliance built in.\n\n## Seeking deeper human-AI partnerships\nDevSecOps professionals don't want AI to take over — they want reliable partnerships. The vast majority (82%) say using agentic AI would increase their job satisfaction, and 43% envision an ideal future with a 50/50 split between human and AI contributions. They're ready to trust AI with 37% of their daily tasks without human review, particularly for documentation, test writing, and code reviews.\n\nWhat we heard resoundingly from DevSecOps professionals is that AI won't replace them; rather, it will fundamentally reshape their roles. 83% of DevSecOps professionals believe AI will significantly change their work within five years, and notably, 76% think this will create more engineering jobs, not fewer. As coding becomes easier with AI, engineers who can architect systems, ensure quality, and apply business context will be in high demand.\n\nCritically, 88% agree there are essential human qualities that AI will never fully replace, including creativity, innovation, collaboration, and strategic vision.\n\nSo how can organizations bridge the gap between AI’s promise and the reality of fragmented workflows?\n\n## Join us at GitLab Transcend: Explore how to drive real value with agentic AI\nOn February 10, 2026, GitLab will be hosting Transcend, where we'll reveal how intelligent orchestration transforms AI-powered software development. You'll get a first look at GitLab's upcoming product roadmap and learn how teams are solving real-world challenges by modernizing development workflows with AI.\n\nOrganizations winning in this new era balance AI adoption with security, compliance, and platform consolidation. AI offers genuine productivity gains when implemented thoughtfully — not by replacing human developers, but by freeing DevSecOps professionals to focus on strategic thinking and creative innovation.\n\n[Register for Transcend today](https://about.gitlab.com/events/transcend/virtual/) to secure your spot and discover how intelligent orchestration can help your software teams stay in flow.",[26,744,745],"DevOps platform","security",{"featured":30,"template":15,"slug":747},"ai-is-reshaping-devsecops-attend-gitlab-transcend-to-see-whats-next",{"content":749,"config":760},{"title":750,"description":751,"authors":752,"heroImage":754,"date":755,"body":756,"category":11,"tags":757},"Atlassian ending Data Center as GitLab maintains deployment choice","As Atlassian transitions Data Center customers to cloud-only, GitLab presents a menu of deployment choices that map to business needs.",[753],"Emilio Salvador","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098354/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%281%29_5XrohmuWBNuqL89BxVUzWm_1750098354056.png","2025-10-07","Change is never easy, especially when it's not your choice. Atlassian's announcement that [all Data Center products will reach end-of-life by March 28, 2029](https://www.atlassian.com/blog/announcements/atlassian-ascend), means thousands of organizations must now reconsider their DevSecOps deployment and infrastructure. But you don't have to settle for deployment options that don't fit your needs. GitLab maintains your freedom to choose — whether you need self-managed for compliance, cloud for convenience, or hybrid for flexibility — all within a single AI-powered DevSecOps platform that respects your requirements.\n\nWhile other vendors force migrations to cloud-only architectures, GitLab remains committed to supporting the deployment choices that match your business needs. Whether you're managing sensitive government data, operating in air-gapped environments, or simply prefer the control of self-managed deployments, we understand that one size doesn't fit all.\n\n## The cloud isn't the answer for everyone\n\nFor the many companies that invested millions of dollars in Data Center deployments, including those that migrated to Data Center [after its Server products were discontinued](https://about.gitlab.com/blog/atlassian-server-ending-move-to-a-single-devsecops-platform/), this announcement represents more than a product sunset. It signals a fundamental shift away from customer-centric architecture choices, forcing enterprises into difficult positions: accept a deployment model that doesn't fit their needs, or find a vendor that respects their requirements.\n\nMany of the organizations requiring self-managed deployments represent some of the world's most important organizations: healthcare systems protecting patient data, financial institutions managing trillions in assets, government agencies safeguarding national security, and defense contractors operating in air-gapped environments.\n\nThese organizations don't choose self-managed deployments for convenience; they choose them for compliance, security, and sovereignty requirements that cloud-only architectures simply cannot meet. Organizations operating in closed environments with restricted or no internet access aren't exceptions — they represent a significant portion of enterprise customers across various industries.\n\n![GitLab vs. Atlassian comparison table](https://res.cloudinary.com/about-gitlab-com/image/upload/v1759928476/ynl7wwmkh5xyqhszv46m.jpg)\n\n## The real cost of forced cloud migration goes beyond dollars\n\nWhile cloud-only vendors frame mandatory migrations as \"upgrades,\" organizations face substantial challenges beyond simple financial costs:\n\n* **Lost integration capabilities:** Years of custom integrations with legacy systems, carefully crafted workflows, and enterprise-specific automations become obsolete. Organizations with deep integrations to legacy systems often find cloud migration technically infeasible.\n\n* **Regulatory constraints:** For organizations in regulated industries, cloud migration isn't just complex — it's often not permitted. Data residency requirements, air-gapped environments, and strict regulatory frameworks don't bend to vendor preferences. The absence of single-tenant solutions in many cloud-only approaches creates insurmountable compliance barriers.\n\n* **Productivity impacts:** Cloud-only architectures often require juggling multiple products: separate tools for planning, code management, CI/CD, and documentation. Each tool means another context switch, another integration to maintain, another potential point of failure. GitLab research shows [30% of developers spend at least 50% of their job maintaining and/or integrating their DevSecOps toolchain](https://about.gitlab.com/developer-survey/). Fragmented architectures exacerbate this challenge rather than solving it.\n\n## GitLab offers choice, commitment, and consolidation\n\nEnterprise customers deserve a trustworthy technology partner. That's why we've committed to supporting a range of deployment options — whether you need on-premises for compliance, hybrid for flexibility, or cloud for convenience, the choice remains yours. That commitment continues with [GitLab Duo](https://about.gitlab.com/gitlab-duo-agent-platform/), our AI solution that supports developers at every stage of their workflow.\n\nBut we offer more than just deployment flexibility. While other vendors might force you to cobble together their products into a fragmented toolchain, GitLab provides everything in a **comprehensive AI-native DevSecOps platform**. Source code management, CI/CD, security scanning, Agile planning, and documentation are all managed within a single application and a single vendor relationship.\n\nThis isn't theoretical. When Airbus and [Iron Mountain](https://about.gitlab.com/customers/iron-mountain/) evaluated their existing fragmented toolchains, they consistently identified challenges: poor user experience, missing functionalities like built-in security scanning and review apps, and management complexity from plugin troubleshooting. **These aren't minor challenges; they're major blockers for modern software delivery.**\n\n## Your migration path: Simpler than you think\n\nWe've helped thousands of organizations migrate from other vendors, and we've built the tools and expertise to make your transition smooth:\n\n* **Automated migration tools:** Our [Bitbucket Server importer](https://docs.gitlab.com/user/import/bitbucket_server/) brings over repositories, pull requests, comments, and even Large File Storage (LFS) objects. For Jira, our [built-in importer](https://docs.gitlab.com/user/project/import/jira/) handles issues, descriptions, and labels, with professional services available for complex migrations.\n\n* **Proven at scale:** A 500 GiB repository with 13,000 pull requests, 10,000 branches, and 7,000 tags is likely to [take just 8 hours to migrate](https://docs.gitlab.com/user/import/bitbucket_server/) from Bitbucket to GitLab using parallel processing.\n\n* **Immediate ROI:** A [Forrester Consulting Total Economic Impact™ study commissioned by GitLab](https://about.gitlab.com/resources/study-forrester-tei-gitlab-ultimate/) found that investing in GitLab Ultimate confirms these benefits translate to real bottom-line impact, with a three-year 483% ROI, 5x time saved in security related activities, and 25% savings in software toolchain costs.\n\n## Start your journey to a unified DevSecOps platform\n\nForward-thinking organizations aren't waiting for vendor-mandated deadlines. They're evaluating alternatives now, while they have time to migrate thoughtfully to platforms that protect their investments and deliver on promises.\n\nOrganizations invest in self-managed deployments because they need control, compliance, and customization. When vendors deprecate these capabilities, they remove not just features but the fundamental ability to choose environments matching business requirements.\n\nModern DevSecOps platforms should offer complete functionality that respects deployment needs, consolidates toolchains, and accelerates software delivery, without forcing compromises on security or data sovereignty.\n\n[Talk to our sales team](https://about.gitlab.com/sales/) today about your migration options, or explore our [comprehensive migration resources](https://about.gitlab.com/move-to-gitlab-from-atlassian/) to see how thousands of organizations have already made the switch.\n\nYou also can [try GitLab Ultimate with GitLab Duo Enterprise](https://about.gitlab.com/free-trial/devsecops/) for free for 30 days to see what a unified DevSecOps platform can do for your organization.",[578,571,758,759],"product","features",{"featured":30,"template":15,"slug":761},"atlassian-ending-data-center-as-gitlab-maintains-deployment-choice",{"promotions":763},[764,778,789,800],{"id":765,"categories":766,"header":768,"text":769,"button":770,"image":775},"ai-modernization",[767],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":771,"config":772},"Get your AI maturity score",{"href":773,"dataGaName":774,"dataGaLocation":249},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":776},{"src":777},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":779,"categories":780,"header":781,"text":769,"button":782,"image":786},"devops-modernization",[758,11],"Are you just managing tools or shipping innovation?",{"text":783,"config":784},"Get your DevOps maturity score",{"href":785,"dataGaName":774,"dataGaLocation":249},"/assessments/devops-modernization-assessment/",{"config":787},{"src":788},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":790,"categories":791,"header":792,"text":769,"button":793,"image":797},"security-modernization",[745],"Are you trading speed for security?",{"text":794,"config":795},"Get your security maturity score",{"href":796,"dataGaName":774,"dataGaLocation":249},"/assessments/security-modernization-assessment/",{"config":798},{"src":799},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":801,"paths":802,"header":805,"text":806,"button":807,"image":812},"github-azure-migration",[803,804],"migration-from-azure-devops-to-gitlab","integrating-azure-devops-scm-and-gitlab","Is your team ready for GitHub's Azure move?","GitHub is already rebuilding around Azure. Find out what it means for you.",{"text":808,"config":809},"See how GitLab compares to GitHub",{"href":810,"dataGaName":811,"dataGaLocation":249},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":813},{"src":788},{"header":815,"blurb":816,"button":817,"secondaryButton":822},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":818,"config":819},"Get your free trial",{"href":820,"dataGaName":57,"dataGaLocation":821},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":512,"config":823},{"href":61,"dataGaName":62,"dataGaLocation":821},1777493585813]